To prove the effectiveness of our data augmentation technique we perform extensive experiments across wide range of popular models and Image classification datasets. We select four variants each of ResNet [1]}, DenseNet [2]} and EfficientNet [3]} architectures. We selected these particular architecture since they provide substantial variation in their architectural concepts. The individual variants in each architecture help us test the method across different depths/sizes of each architecture. For image classification datasets we chose CIFAR-10 and CIFAR-100 [4]} as these are widely used benchmark datasets to compare our method against.
